Introduction to Scientific Computing II

Slides:



Advertisements
Similar presentations
CSE Seminar Benefits of Hierarchy and Adaptivity Preliminary Discussion Dr. Michael Bader / Dr. Miriam Mehl Institut für Informatik Scientific Computing.
Advertisements

Institut für Informatik Scientific Computing in Computer Science Practical Course SC & V Time Discretisation Dr. Miriam Mehl.
CSE 245: Computer Aided Circuit Simulation and Verification Matrix Computations: Iterative Methods (II) Chung-Kuan Cheng.
CISC 489/689 Spring 2009 University of Delaware
Scientific Computing Lab Results Worksheet 3 Dr. Miriam Mehl Institut für Informatik Scientific Computing in Computer Science.
Improvement of a multigrid solver for 3D EM diffusion Research proposal final thesis Applied Mathematics, specialism CSE (Computational Science and Engineering)
Siddharth Choudhary.  Refines a visual reconstruction to produce jointly optimal 3D structure and viewing parameters  ‘bundle’ refers to the bundle.
1 Iterative Solvers for Linear Systems of Equations Presented by: Kaveh Rahnema Supervisor: Dr. Stefan Zimmer
1 Numerical Solvers for BVPs By Dong Xu State Key Lab of CAD&CG, ZJU.
Geometric (Classical) MultiGrid. Hierarchy of graphs Apply grids in all scales: 2x2, 4x4, …, n 1/2 xn 1/2 Coarsening Interpolate and relax Solve the large.
1 Minimum Ratio Contours For Meshes Andrew Clements Hao Zhang gruvi graphics + usability + visualization.
Influence of (pointwise) Gauss-Seidel relaxation on the error Poisson equation, uniform grid Error of initial guess Error after 5 relaxation Error after.
Ekaterina Smorodkina and Dr. Daniel Tauritz Department of Computer Science Power Grid Protection through Rapid Response Control of FACTS Devices.
Unconstrained Optimization Problem
September 23, 2010Neural Networks Lecture 6: Perceptron Learning 1 Refresher: Perceptron Training Algorithm Algorithm Perceptron; Start with a randomly.
Lamps of Aladdin1 Moving Mesh Adaptation Techniques Todd Phillips Gary Miller Mark Olah.
1 M. Bronstein Multigrid multidimensional scaling Multigrid Multidimensional Scaling Michael M. Bronstein Department of Computer Science Technion – Israel.
Exercise where Discretize the problem as usual on square grid of points (including boundaries). Define g and f such that the solution to the differential.
Implementation of Nonlinear Conjugate Gradient Method for MLP Matt Peterson ECE 539 December 10, 2001.
CS B553: A LGORITHMS FOR O PTIMIZATION AND L EARNING Gradient descent.
Multigrid for Nonlinear Problems Ferien-Akademie 2005, Sarntal, Christoph Scheit FAS, Newton-MG, Multilevel Nonlinear Method.
Introduction to Scientific Computing II From Gaussian Elimination to Multigrid – A Recapitulation Dr. Miriam Mehl.
CS 478 – Tools for Machine Learning and Data Mining Backpropagation.
Multigrid Computation for Variational Image Segmentation Problems: Multigrid approach  Rosa Maria Spitaleri Istituto per le Applicazioni del Calcolo-CNR.
Rutgers, The State University of New Jersey Iterative Embedding with Robust Correction using Feedback of Error Observed Praneeth Vepakomma 1 Ahmed Elgammal.
Introduction to Scientific Computing II Overview Michael Bader.
Introduction to Scientific Computing II Multigrid Dr. Miriam Mehl Institut für Informatik Scientific Computing In Computer Science.
Introduction to Scientific Computing II Multigrid Dr. Miriam Mehl.
1 Chapter 6 General Strategy for Gradient methods (1) Calculate a search direction (2) Select a step length in that direction to reduce f(x) Steepest Descent.
Introduction to Scientific Computing II
Final Exam Review CS479/679 Pattern Recognition Dr. George Bebis 1.
Optimization in Engineering Design 1 Introduction to Non-Linear Optimization.
INTRO TO OPTIMIZATION MATH-415 Numerical Analysis 1.
Scientific Computing Lab Outlook / State of Research Dr. Miriam Mehl Institut für Informatik Scientific Computing in Computer Science.
Regularization of energy-based representations Minimize total energy E p (u) + (1- )E d (u,d) E p (u) : Stabilizing function - a smoothness constraint.
Scientific Computing Lab Organization Dr. Miriam Mehl Institut für Informatik Scientific Computing in Computer Science.
High Performance Computing Seminar II Parallel mesh partitioning with ParMETIS Parallel iterative solvers with Hypre M.Sc. Caroline Mendonça Costa.
Fall 2004 Backpropagation CS478 - Machine Learning.
Scientific Computing Lab
Scientific Computing Lab
MultiGrid.
CSE 245: Computer Aided Circuit Simulation and Verification
Introduction to Multigrid Method
Introduction to Scientific Computing II
Pressure Poisson Equation
Standard Slope Function
Steepest Descent Algorithm: Step 1.
Scientific Computing Lab
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Instructor :Dr. Aamer Iqbal Bhatti
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Scientific Computing Lab
Redundant Ghost Nodes in Jacobi
Introduction to Scientific Computing II
(Analyses Acceleration) (with Nested Iterations)
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Spatial Discretisation
Numerical Computation and Optimization
Nonlinear Conjugate Gradient Method for Supervised Training of MLP
Presentation transcript:

Introduction to Scientific Computing II Multigrid & Steepest Descent Dr. Miriam Mehl

Multigrid – Some Rules smoother optimal smoothing not!!! optimal convergence small number of smoothing iterations!

Multigrid – Some Rules grid coarsening standard: doubling of h exceptions: unisotropic operators adaptively refined grids unstructured grids/general SLEs

Multigrid – Some Rules restriction/interpolation order of restriction + order of interpolation > order of discretisation

Multigrid – Some Rules V-cycle faster W-cycle more robust

Steepest Descent – Basic Idea solution of SLE minimization iterative one-dimensional minima direction of steepest descent?

Steepest Descent – Principle

Steepest Descent – Principle

Steepest Descent – Algorithm

Steepest Descent – Algorithm II

Steepest Descent – Example initial error after 1 iteration after 10 iterations

Steepest Descent – Example 1/128 1/64 1/32 1/16 h 48,629 11,576 2,744 646 iterations